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An enhanced segmentation technique and improved Support vector machine classifier for facial image recognition

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International Journal of Intelligent Computing and Cybernetics 2022年第2期15卷 302-317页

作者： Rangayya Virupakshappa Nagabhushan Patil Sharnbasva University KalaburagiIndia Poojya Doddappa Appa College of Engineering GulbargaIndia

Purpose-One of the challenging issues in computer vision and pattern recognition is face image recognition.Several studies based on face recognition were introduced in the past decades,but it has few classification issues in terms of poor performances.Hence,the authors proposed a novel model for face recognition.Design/methodology/approach-The proposed method consists of four major sections such as data acquisition,segmentation,feature extraction and recognition.Initially,the images are transferred into grayscale images,and they pose issues that are eliminated by resizing the input images.The contrast limited adaptive histogram equalization(CLAHE)utilizes the image preprocessing step,thereby eliminating unwanted noise and improving the image contrast level.Second,the active contour and level set-based segmentation(ALS)with neural network(NN)or ALS with NN algorithm is used for facial image segmentation.Next,the four major kinds of feature descriptors are dominant color structure descriptors,scale-invariant feature transform descriptors,improved center-symmetric local binary patterns(ICSLBP)and histograms of gradients(HOG)are based on clour and texture features.Finally,the Support vector machine(SVM)with modified random forest(MRF)model for facial image recognition.Findings-Experimentally,the proposed method performance is evaluated using different kinds of evaluation criterions such as accuracy,similarity index,dice similarity coefficient,precision,recall and F-score results.However,the proposed method offers superior recognition performances than other state-of-art methods.Further face recognition was analyzed with the metrics such as accuracy,precision,recall and F-score and attained 99.2,96,98 and 96%,respectively.Originality/value-The good facial recognition method is proposed in this research work to overcome threat to privacy,violation of rights and provide better security of data.

关键词： Face recognition Active contour and Level set-based segmentation Neural network algorithm Support vector machine Modified random forest classifier

HHO optimized Support vector machine classifier for traditional Chinese medicine syndrome differentiation of diabetic retinopathy

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International Journal of Ophthalmology(English edition) 2024年第6期17卷 991-1000页

作者： Li Xiao Cheng-Wu Wang Ying Deng Yi-Jing Yang Jing Lu Jun-Feng Yan Qing-Hua Peng School of Chinese Medicine Hunan University of Chinese MedicineChangsha 410208Hunan ProvinceChina School of Informatics Hunan University of Chinese MedicineChangsha 410208Hunan ProvinceChina Hunan Provincial Key Laboratory for Prevention and Treatment of Ophthalmology and Otolaryngology Diseases with Chinese Medicine Hunan University of Chinese MedicineChangsha 410208Hunan ProvinceChina Hunan Provincial Engineering and Technological Research Center for Prevention and Treatment of Ophthalmology and Otolaryngology Diseases with Chinese Medicine and Protecting Visual Function Hunan University of Chinese MedicineChangsha 410208Hunan ProvinceChina

AIM:To develop a classifier for traditional Chinese medicine(TCM)syndrome differentiation of diabetic retinopathy(DR),using optimized machine learning algorithms,which can provide the basis for TCM objective and intelligent syndrome differentiation.METHODS:Collated data on real-world DR cases were collected.A variety of machine learning methods were used to construct TCM syndrome classification model,and the best performance was selected as the basic model.Genetic Algorithm(GA)was used for feature selection to obtain the optimal feature combination.Harris Hawk Optimization(HHO)was used for parameter optimization,and a classification model based on feature selection and parameter optimization was constructed.The performance of the model was compared with other optimization algorithms.The models were evaluated with accuracy,precision,recall,and F1 score as indicators.RESULTS:Data on 970 cases that met screening requirements were collected.Support vector machine(SVM)was the best basic classification model.The accuracy rate of the model was 82.05%,the precision rate was 82.34%,the recall rate was 81.81%,and the F1 value was 81.76%.After GA screening,the optimal feature combination contained 37 feature values,which was consistent with TCM clinical practice.The model based on optimal combination and SVM(GA_SVM)had an accuracy improvement of 1.92%compared to the basic classifier.SVM model based on HHO and GA optimization(HHO_GA_SVM)had the best performance and convergence speed compared with other optimization algorithms.Compared with the basic classification model,the accuracy was improved by 3.51%.CONCLUSION:HHO and GA optimization can improve the model performance of SVM in TCM syndrome differentiation of DR.It provides a new method and research idea for TCM intelligent assisted syndrome differentiation.

关键词： traditional Chinese medicine diabetic retinopathy Harris Hawk Optimization Support vector machine syndrome differentiation

Analysis of Water Pollution Causes and Control Countermeasures in Liaohe Estuary via Support vector machine Particle Swarm Optimization under Deep Learning

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Computer Modeling in Engineering & Sciences 2022年第1期130卷 315-329页

作者： Guize Liu Jinqing Ye Yuan Chen Xiaolong Yang Yanbin Gu College of Environmental Science and Engineering Ocean University of ChinaQingdao266003China NationalMarine EnvironmentalMonitoring Center Ministry of Ecology and EnvironmentDalian116023China

This study explores the loss or degradation of the ecosystem and its service function in the Liaohe estuary coastal zone due to the deterioration ofwater quality.Aprediction systembased on support vectormachine(SVM)-particle swarm optimization(PSO)(SVM-PSO)algorithm is proposed under the background of deep learning.SVM-PSO algorithm is employed to analyze the pollution status of the Liaohe estuary,so is the difference in water pollution of different sea consuming types.Based on the analysis results for causes of pollution,the control countermeasures of water pollution in Liaohe estuary are put forward.The results suggest that the water pollution index prediction model based on SVM-PSO algorithm shows the maximum error of 2.41%,the average error of 1.24%in predicting the samples,the root mean square error(RMSE)of 5.36×10^(−4),and the square of correlation coefficient of 0.91.Therefore,the prediction system in this study is feasible.At present,the water pollution status of Liaohe estuary is of moderate and severe levels of eutrophication,and the water pollution status basically remains at the level of mild pollution.The general trend is fromphosphorus moderate restricted eutrophication to phosphorus restricted potential eutrophication.To sumup,the SVM-PSO algorithm shows good sewage prediction ability,which can be applied and promoted in water pollution control and has reliable reference significance.

关键词： Water pollution countermeasure analysis Support vector machine particle swarm optimization

Artificial intelligence method for predicting the maximum stress of an off-center casing under non-uniform ground stress with Support vector machine

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Science China(Technological Sciences) 2020年第12期63卷 2553-2561页

作者： DI QinFeng WU ZhiHao CHEN Tao CHEN Feng WANG WenChang QIN GuangXu CHEN Wei Shanghai Institute of Applied Mathematics and Mechanics School of Mechanics and Engineering ScienceShanghai UniversityShanghai 200444China School of Computer Engineering and Science Shanghai UniversityShanghai 200444China School of Mechatronics Engineering and Automaton Shanghai UniversityShanghai 200444China

The situation of an off-center casing under non-uniform ground stress can occur in the process of drilling a salt-gypsum formation,and the related casing stress calculation has not yet been solved analytically. In addition,the experimental equipment in many cases cannot meet the actual conditions and the experimental cost is very high. These comprehensive factors cause the existing casing design to not meet the actual conditions and cause casing deformation,affecting the drilling operation in Tarim oil field. The finite element method is the only effective method to solve this problem at present,but the re-modelling process is time-consuming because of the changes in the parameters,such as the cement properties,casing centrality,and the casing size. In this article,an artificial intelligence method based on Support vector machine(SVM) to predict the maximum stress of an offcenter casing under non-uniform ground stress has been proposed. After a program based on a radial basis function(RBF)-support vector regression(SVR)(ε-SVR) model was established and validated,we constructed a data sample with a capacity of 120 by using the finite element method,which could meet the demand of the nine-factor ε-SVR model to predict the maximum stress of the casing. The results showed that the artificial intelligence prediction method proposed in this manuscript had satisfactory prediction accuracy and could be effectively used to predict the maximum stress of an off-center casing under complex downhole conditions.

关键词： Support vector machine maximum stress off-center casing non-uniform ground stress oil and gas wells

SCADA Data-Based Support vector machine for False Alarm Identification for Wind Turbine Management

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Intelligent Automation & Soft Computing 2023年第9期37卷 2595-2608页

作者： Ana María Peco Chacón Isaac Segovia Ramírez Fausto Pedro García Márquez Ingenium Research Group Universidad Castilla-La ManchaCiudad Real13071Spain

Maintenance operations have a critical influence on power gen-eration by wind turbines(WT).Advanced algorithms must analyze large volume of data from condition monitoring systems(CMS)to determine the actual working conditions and avoid false alarms.This paper proposes different Support vector machine(SVM)algorithms for the prediction and detection of false alarms.K-Fold cross-validation(CV)is applied to evaluate the classification reliability of these algorithms.Supervisory Control and Data Acquisition(SCADA)data from an operating WT are applied to test the proposed approach.The results from the quadratic SVM showed an accuracy rate of 98.6%.Misclassifications from the confusion matrix,alarm log and maintenance records are analyzed to obtain quantitative information and determine if it is a false alarm.The classifier reduces the number of false alarms called misclassifications by 25%.These results demonstrate that the proposed approach presents high reliability and accuracy in false alarm identification.

关键词： machine learning classification Support vector machine false alarm wind turbine cross-validation

Support vector machine based machine learning method for GS 8QAM constellation classification in seamless integrated fiber and visible light communication system

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Science China(Information Sciences) 2020年第10期63卷 182-193页

作者： Wenqing NIU Yinaer HA Nan CHI Key Laboratory for Information Science of Electromagnetic Waves (MoE) Fudan University

Visible light communication(VLC) network over optical fiber has become a potential candidate in ultra-high speed indoor wireless communication. To mitigate signal distortion accumulated in optical fiber and VLC channel, we present to utilize Support vector machine(SVM) for constellation classification in two kinds of geometrically-shaped 8 QAM(quadrature amplitude modulation) seamless integrated fiber and VLC system. We introduce 4 sub-bands to simulate multi-user. Experimental results show that system performance can be significantly improved, and transmission at-2.5 dB m input optical power under 7%forward error correction(FEC) threshold can be realized employing Circular(7, 1) geometrically-shaped8 QAM and SVM. At overall capacity of 960 Mbps, Q-factor increases by up to 11.5 dB.

关键词： visible light communication VLC Support vector machine SVM geometrically shaping GS VLC network constellation classification

Support vector machine Assisted GPS Navigation in Limited Satellite Visibility

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Computers, Materials & Continua 2021年第10期69卷 555-574页

作者： Dah-Jing Jwo Jia-Chyi Wu Kuan-Lin Ho Department of Communications Navigation and Control EngineeringTaiwan Ocean UniversityKeelung202301TaiwanChina

This paper investigates performance improvement via the incorporation of the Support vector machine(SVM)into the vector tracking loop(VTL)for the Global Positioning System(GPS)in limited satellite visibility.Unlike the traditional scalar tracking loop(STL),the tracking and navigation modules in the VTL are not independent anymore since the user’s position can be determined by using the information from other satellites and can be predicted on the basis of the states of the user.The method proposed in this paper makes use of the SVM to bridge the GPS signal and prevent the error growth due to signal outage.Similar to the neural network,the SVM is motivated by its ability to approximate an unknown nonlinear input-output mapping through supervised training.The SVM is employed for predicting adequate numerical control oscillator(NCO)inputs,i.e.,providing better prediction of residuals for the Doppler frequency and code phase in order to maintain regular operation of the navigation system.When the navigation processing is in good condition,the SVM is at the training stage,and the output information from the discriminator and navigation filter is adopted as the inputs.Other machine learning(ML)algorithms such as the radial basis function neural network(RBFNN)and the Adaptive Network-Based Fuzzy Inference System(ANFIS)are employed for comparison.Performance evaluation for the SVM assisted architecture as compared to the RBFNNand ANFIS-assisted methods and the un-assisted VTL will be carried out and the performance evaluation during GPS signal outage will be presented.The proposed design is very useful for navigation during the environment of limited satellite visibility to effectively overcome the problem in the environment of GPS outage.

关键词： Global positioning system Support vector machine machine learning vector tracking loop signal outage

Neural network and Support vector machine models for the prediction of the liquefaction-induced uplift displacement of tunnels

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Underground Space 2021年第2期6卷 126-133页

作者： Gang Zheng Wenbin Zhang Wengang Zhang Haizuo Zhou Pengbo Yang School of Civil Engineering Tianjin UniversityTianjin 300072China Key Laboratory of Coast Civil Structure Safety Tianjin UniversityMinistry of EducationTianjin 300072China State Key Laboratory of Hydraulic Engineering Simulation and Safety Tianjin UniversityTianjin 300072China National Joint Engineering Research Center of Geohazards Prevention in Reservoir Area Environment School of Civil EngineeringChongqing UniversityChongqing 400045China

Tunnels buried in liquefiable deposits are vulnerable to liquefaction-induced uplift damage during earthquakes.This paper presents Support vector machine(SVM)and artificial neural network(ANN)models to predict the liquefaction-induced uplift displacement of tunnels based on artificial databases generated by the finite difference method.The performance of the SVM and ANN models was assessed using statistical parameters,including the coefficient of determination R^(2),the mean absolute error,and the root mean squared error.Applications for the above-mentioned approaches are compared and discussed.A relative importance analysis was adopted to quantify the sensitivity of each input variable.The precision of the presented models is demonstrated using centrifuge test results from previous studies.

关键词： Artificial neural network Support vector machine Liquefaction Uplift displacement

A real-time neutron-gamma discriminator based on the Support vector machine method for the time-of-flight neutron spectrometer

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Plasma Science and Technology 2018年第4期20卷 170-175页

作者：张伟吴彤宇郑博文李世平张轶泼阴泽杰 State Key Laboratory of Particle Detection and Electronics University of Science and Technology ofChina Hefei 230026 People's Republic of China Department of Modem Physics University of Science and Technology of China Hefei 230026 People'sRepublic of China Southwestern Institute of Physics Chengdu 610041 People's Republic of China

A new neutron-gamma discriminator based on the Support vector machine(SVM) method is proposed to improve the performance of the time-of-flight neutron spectrometer. The neutron detector is an EJ-299-33 plastic scintillator with pulse-shape discrimination(PSD) property. The SVM algorithm is implemented in field programmable gate array(FPGA) to carry out the real-time sifting of neutrons in neutron-gamma mixed radiation fields. This study compares the ability of the pulse gradient analysis method and the SVM method. The results show that this SVM discriminator can provide a better discrimination accuracy of 99.1%. The accuracy and performance of the SVM discriminator based on FPGA have been evaluated in the experiments. It can get a figure of merit of 1.30.

关键词： plasma diagnosis Support vector machine pulse-shape discrimination TOF spectrometer